An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Cheng, Lu; Ramchandran, Siddharth; Vatanen, Tommi; Lietzén, Niina; Lahesmaa, Riitta; Vehtari, Aki; Lähdesmäki, Harri

doi:10.1038/s41467-019-09785-8

Cited by 87 publications

(69 citation statements)

References 16 publications

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“…Finally, Gaussian processes-based non-parametric modelling approaches are becoming increasingly popular in the analysis of longitudinal omics datasets and external variables for their flexibility and accuracy (e.g. [78] ).…”

Section: Methodsmentioning

confidence: 99%

Experimental and computational approaches to unravel microbial community assembly

Cárcer

2020

Computational and Structural Biotechnology Journal

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Microbial communities have a preponderant role in the life support processes of our common home planet Earth. These extremely diverse communities drive global biogeochemical cycles, and develop intimate relationships with most multicellular organisms, with a significant impact on their fitness. Our understanding of their composition and function has enjoyed a significant thrust during the last decade thanks to the rise of high-throughput sequencing technologies. Intriguingly, the diversity patterns observed in nature point to the possible existence of fundamental community assembly rules. Unfortunately, these rules are still poorly understood, despite the fact that their knowledge could spur a scientific, technological, and economic revolution, impacting, for instance, agricultural, environmental, and health-related practices. In this minireview, I recapitulate the most important wet lab techniques and computational approaches currently employed in the study of microbial community assembly, and briefly discuss various experimental designs. Most of these approaches and considerations are also relevant to the study of microbial microevolution, as it has been shown that it can occur in ecological relevant timescales. Moreover, I provide a succinct review of various recent studies, chosen based on the diversity of ecological concepts addressed, experimental designs, and choice of wet lab and computational techniques. This piece aims to serve as a primer to those new to the field, as well as a source of new ideas to the more experienced researchers.

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Section: Methodsmentioning

confidence: 99%

Experimental and computational approaches to unravel microbial community assembly

Cárcer

2020

Computational and Structural Biotechnology Journal

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“…Recently, we have developed GP based methods to implement Bayesian non-parametrics for longitudinal studies [18,19] that can also be applied to data from paired longitudinal designs. However, posterior sampling for such models has high computational cost.…”

Section: Previous Methodsmentioning

confidence: 99%

PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies

Vantini

Mannerström

Rautio

et al. 2020

Preprint

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We propose PairGP, a non-stationary Gaussian process method to compare gene expression timeseries across several conditions that can account for paired longitudinal study designs and can identify groups of conditions that have different gene expression dynamics. We demonstrate the method on both simulated data and previously unpublished RNA-seq time-series with five conditions. The results show the advantage of modeling the pairing effect to better identify groups of conditions with different dynamics. The implementations is available at https://github.com/michelevantini/PairGP

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“…Interestingly, their experiments show that typically only a few orders of interactions are important. Alternatively, the choice of terms in the additive GP may be application driven; more recently, this is the approach taken in Cheng et al (2019) for longitudinal biomedical data. Another interesting direction in Gilboa et al (2015) constructs projected additive GPs through first-order functions of linear projections of the inputs.…”

Section: Sparse Non-stationary Additive Modelsmentioning

confidence: 99%

Posterior inference for sparse hierarchical non-stationary models

Monterrubio-Gómez

Roininen

Wade

et al. 2020

Computational Statistics & Data Analysis

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Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed. While removing this assumption can improve prediction, fitting such models is challenging. In this work, hierarchical models are constructed based on Gaussian Markov random fields with stochastic spatially varying parameters. Importantly, this allows for non-stationarity while also addressing the computational burden through a sparse banded representation of the precision matrix. In this setting, efficient Markov chain Monte Carlo (MCMC) sampling is challenging due to the strong coupling a posteriori of the parameters and hyperparameters. We develop and compare three adaptive MCMC schemes and make use of banded matrix operations for faster inference. Furthermore, a novel extension to multi-dimensional settings is proposed through an additive structure that retains the flexibility and scalability of the model, while also inheriting interpretability from the additive approach. A thorough assessment of the efficiency and accuracy of the methods in nonstationary settings is presented for both simulated experiments and a computer emulation problem.

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An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Cited by 87 publications

References 16 publications

Experimental and computational approaches to unravel microbial community assembly

Experimental and computational approaches to unravel microbial community assembly

PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies

Posterior inference for sparse hierarchical non-stationary models

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